In the more comprehensive climate modeling settings we target, data will be given from earth observations and from local high-resolution simulations [1]}. In these settings, model error leads to deficiencies when comparing model evaluations to data, leading to structural biases and uncertainty that must be quantified. Structural model errors can be quantified with a flexible hierarchical Gaussian process regression that learns a non-parametric form of the model deficiency, as demonstrated in prototype problems in [2]}. This approach represents model error in an interpretable
fashion, as part of the model itself, rather than in the data space as pioneered
in [3]}.
